{"id":"W3157781775","doi":"10.48550/arxiv.2104.11700","title":"Robust Federated Learning by Mixture of Experts","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007737676,0.001733487,0.003061462,0.00125042,0.0006374032,0.002230058,0.003573155,0.002888195,0.001822914],"category_scores_gemma":[0.01890029,0.001014555,0.001808648,0.001399668,0.00189107,0.004128012,0.003755462,0.003386379,0.0008592923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068988,"about_ca_system_score_gemma":0.001378114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002528175,"about_ca_topic_score_gemma":0.002073857,"domain_scores_codex":[0.9954698,0.00200675,0.0002076455,0.001109283,0.0007924395,0.000414054],"domain_scores_gemma":[0.9910261,0.005109859,0.0007830653,0.001511632,0.001235365,0.0003339465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003388537,0.0001067346,0.001276293,0.00007533922,0.0001895038,0.000102546,0.0001013285,0.909735,0.001624968,0.01660182,0.001458371,0.06838931],"study_design_scores_gemma":[0.000005232856,0.00002444654,0.00004601812,0.000004242755,0.000008155133,0.00001776065,0.000004075236,0.9931957,0.0004752689,0.006060026,0.0001528453,0.000006346683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006661083,0.0001526903,0.9922739,0.0001327521,0.00002428665,0.0000223744,0.00003497381,0.0003045794,0.0003933033],"genre_scores_gemma":[0.7172096,0.0003119656,0.2770095,0.0005220545,0.0001652097,0.0001912997,0.0003356975,0.0001467526,0.004107844],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007737676,"threshold_uncertainty_score":0.04092127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07879473565408382,"score_gpt":0.1955513996947937,"score_spread":0.1167566640407099,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}